Claire Birnie

Alumni

Research Scientist

Alumni

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Biography



Research Interests

Claire is a machine learning specialist focussing on the application of data science techniques to subsurface related problems. During her time in industry she developed several ML procedures to aid operational planning, production forecasting, detection of anomalous flow in wells, and microseismic event detection. Her contributions spanned from proof-of-concept research through to implementation of production-ready technologies that required distributed training of large neural networks on cloud computing. She is particularly passionate about demystifying 'Artificial Intelligence' and its potential use within the Energy sector.

Selected Publications

1. A joint inversion-segmentation approach to assisted seismic interpretation, , Submitted.

Ravasi, M., Birnie, C.E.,

Geophysical Journal International, (2021 – In Review)

arXiv:2102.03860

 

2. An introduction to distributed training of deep neural networks for segmentation tasks with large seismic datasets,

Birnie, C.E., Jarraya, H., Hansteen, F.,

Geophysics, (2021 – In Review)

arXiv:2102.13003

 

3.Bidirectional recurrent neural networks for seismic event detection,

Birnie, C.E., Hansteen, F.,

Geophysics, (2021 – In Review)

arXiv:2012.03009

 

4.Generating Custom Word Embeddings for Geoscientific Corpi,

Birnie, C.E., Ravasi, M.,

First Break 38 (7), 61-67 (2020)

 

5.On the importance of benchmarking algorithms under realistic noise conditions,

Birnie, C.E., Chambers, K., Angus, D., Stork, A.L.,

Geophysical Journal International 221 (1), 504-520, (2020)

 

6. Improving the quality and efficiency of operational planning with risk management with ML and NLP,

Birnie, C.E., Sampson, J., Sjaastad, E., Johansen, B., Obrestad, L., Larsen, R., Khamassi, A.,

SPE Offshore Europe Conference and Exhibition, (2019)

 

7. s CO2 injection at Aquistore aseismic­ A combined seismological and geomechanical study of early injection operations,

Stork, A.L., Nixon, C.G., Hawkes, C.D., Birnie, C., White, D.J., Schmitt, D.R. and Roberts, B.,

International Journal of Greenhouse Gas Control 75, 107-124, (2018)

 

8.Seismic arrival enhancement through the use of noise whitening.

Birnie, C., Chambers, K., and Angus, D.,

Physics of the Earth and Planetary Interiors 262, 80-89, (2017)

 

9.Analysis and models of pre-injection surface seismic array noise recorded at the Aquistore carbon storage site.

Birnie, C., Chambers, K., Angus, D., and Stork, A.,

Geophysical Journal International 206 (2), 1246-1260, (2016)

Education

B.Sc., Geophysics and Meteorology, University of Edinburgh, UK, 2014

Microsoft Professional Program in Data Science, 2017

Ph.D., Computational Geophysics, University of Leeds, UK, 2018

Professional Profile

2016-2017: Visiting Researcher, University of Western Australia, Perth, Australia

2017: R&D Intern, Nanometrics, Canada (Remote)

2018-2021: Data Scientist, Digital Center of Excellence, Equinor (née Statoil), Norway

Scientific and Professional Membership

European Association of Geoscientists and Engineers (EAGE)

Society of Exploration Geophysicists (SEG)

 

Associate Editor of SEG Geophysics Journal

Committee member of EAGE AI special community

Awards

Sep 2019 - Finalist for best application of AI, The DataSci & AI Awards

Jun 2017 - Subsurface Machine Learning Hackathon, Best Presentation Award.

Dec 2016 - Codes and Microsoft Scholarship for Professional Program in Data Science.

Aug 2016 - Australian Bicentennial Scholarship Award.

KAUST Affiliations

  • Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC)
  • Physical Science and Engineering Division (PSE)

Research Interests Keywords

Geophysics Deep Learning Signal Processing Statistical modelling Machine Learning